54 lines
2.5 KiB
C#
54 lines
2.5 KiB
C#
using System;
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using Unity.Mathematics;
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/// <summary>
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/// The Pulsar represents a type of neuron that operates based on
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/// the product of its weighted inputs rather than the traditional summation.
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/// Drawing inspiration from the concept of pulsars in astrophysics
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/// —highly magnetized rotating neutron stars that emit beams of radiation—
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/// the Pulsar could symbolize dynamic, focused output based on the interaction of multiple factors.
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/// </summary>
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/// Multiplicative Functionality:
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/// Instead of summing inputs, the Pulsar takes the weighted product of its inputs.
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/// This means that all inputs must be active (non-zero) for the neuron to "pulse" or activate.
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/// Output Behavior:
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/// The output could amplify or diminish depending on the magnitude of the inputs.
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/// The product would be sensitive to small values,
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/// which means that even a small input could significantly lower the overall output if multiplied.
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/// Activation Mechanism:
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/// The activation function can further refine the output from the product result.
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/// For instance, a certain threshold could be used to determine if a pulse occurs.
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/// Modeling Complex Interactions:
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/// The Pulsar could be particularly beneficial for modeling situations where interactions multiply rather than add.
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/// This is useful in fields such as economics (e.g., compound growth models),
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/// biology (e.g., interaction of hormones), and machine learning where multiplicative relationships exist.
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[Serializable]
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public class Pulsar : Neuron {
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public Pulsar(Cluster parent, string name) : base(parent, name) {
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// To prevent mistakes, bias one (instead of zero for standard neurons)
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this.bias = new float3(1, 1, 1);
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}
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public Pulsar(ClusterPrefab parent, string name) : base(parent, name) {
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// To prevent mistakes, bias one (instead of zero for standard neurons)
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this.bias = new float3(1, 1, 1);
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}
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public override Nucleus ShallowCloneTo(Cluster newParent) {
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Pulsar clone = new(newParent, this.name);
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CloneFields(clone);
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return clone;
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}
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public override void UpdateStateIsolated() {
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float3 product = this.bias;
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//Applying the weight factors
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foreach (Synapse synapse in this.synapses) {
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float3 input = synapse.weight * synapse.nucleus.outputValue;
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product *= input;
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}
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// Activation function
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this.outputValue = Activation(product);
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}
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} |